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        七：独热编码 (One-Hot Encoding) - Fainle的博客 | Fainle&#39;s Blog
        
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        七：独热编码 (One-Hot Encoding)
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        <span class="attr">发布于：<span>2020-07-27 09:25</span></span>
        
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        <h3 id="一-概述"><a href="#一-概述" class="headerlink" title="一 . 概述"></a>一 . 概述</h3><p>使用pandas对Dataframe上进行One-Hot Encoding的例子。</p>
<h3 id="二-Pandas-Dataframe-中的热编码。"><a href="#二-Pandas-Dataframe-中的热编码。" class="headerlink" title="二 . Pandas Dataframe 中的热编码。"></a>二 . Pandas Dataframe 中的热编码。</h3><p>首先我们创建一个DataFrame数据集。</p>
<figure class="highlight xl"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"></span><br><span class="line"><span class="keyword">data</span> = pd.DataFrame(&#123;<span class="string">'color'</span>: [<span class="string">'blue'</span>, <span class="string">'red'</span>, <span class="string">'yellow'</span>]&#125;)</span><br></pre></td></tr></table></figure>
<p><img src="/img/1.png" alt=""></p>
<p>pandas上有一个<code>get_dummies</code>方法可以对DataFrame数据，实现我们想要的独热编码功能。</p>
<figure class="highlight xl"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pd.get_dummies(<span class="keyword">data</span>[<span class="string">'color'</span>], <span class="keyword">prefix</span>=<span class="string">'color'</span>)  ## 其中 <span class="keyword">prefix</span> 为新的特征名称前缀</span><br></pre></td></tr></table></figure>
<p><img src="/img/2.png" alt=""></p>
<p>有时候我们可能会出现在预测数据集合里面存在 在训练集合里面没有的特质数据。比如 <code>green</code>，这样会让独热编码出错。这种情况可以使用CategoricalDType方法来处理</p>
<figure class="highlight prolog"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">df[<span class="string">'color'</span>] = df[<span class="string">'color'</span>].astype(pd.<span class="symbol">CategoricalDtype</span>([<span class="string">'blue'</span>, <span class="string">'green'</span>]))</span><br></pre></td></tr></table></figure>
<p>这样其它异常值会被忽略</p>
<p>使用pd.concat可以将新的数据列和原始数据链接起来</p>
<figure class="highlight ini"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attr">df</span> = pd.concat([df, pd.get_dummies(df[<span class="string">'color'</span>], prefix=<span class="string">'color'</span>)], axis=<span class="number">1</span>)</span><br></pre></td></tr></table></figure>
<p>也可以使用 <code>dummy_na=True</code> 参数保留 <code>NaN</code> 值为一个单独的独热编码</p>
<figure class="highlight ini"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="attr">df</span> = pd.concat([df, pd.get_dummies(df[<span class="string">'color'</span>], prefix=<span class="string">'color'</span>, dummy_na=<span class="literal">True</span>)], axis=<span class="number">1</span>)</span><br></pre></td></tr></table></figure>
<p>最后结构如下图</p>
<p><img src="/img/3.png" alt=""></p>

        
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